NNA: Dynamic Vehicle-Terrain Modeling and Control of Lightweight Ground Robots in Snow and Sand

这项研究项目试图从根本上了解轮式车辆在雪地和沙地等松软地面上行驶的动力学。该项目将推导出运动模型，其中包括对车轮和地面之间相互作用的改进描述。这些创新的车型将允许处理，例如，轻型车辆和容易破碎的地形。这些模型将使机器人能够预测它何时面临不可挽回的卡住危险。然后，新的基于模型的控制技术将允许机器人避免这种危险，例如，通过重复压实地形，适当调节拖曳负载的缆力，或者简单地修改计划的路线。这些成果将被部署在移动地面机器人系统中，能够在无人看管的情况下穿越北极地区，携带或拖曳传感器包，以提供“地面真实”数据，以便与卫星或飞机的测量结果进行比较。目前还没有轻量级的自主移动平台可以在北极地形上自主漫游很长一段时间，而不会被低凝聚力的雪块固定住。该项目建立在调查员在南极更均匀、更可预测的地形上使用类似机器人的成功基础上。该项目通过实现对北极地区的长期、长期的科学调查，提高对北极系统对自然和人类活动的反应的了解，从而促进国家的健康、繁荣和福利。该项目还支持NSF关于航行新北极的大想法。最后，该项目将为达特茅斯大学本科生的跨学科工程设计体验提供新的内容，并将为来自美国、丹麦和格陵兰的高中生实践北极科学推广计划提供新材料。现有的车辆-地形模型已经针对重型车辆进行了推导和验证，并且已经表明，轻型车辆的行为与这些模型预测的不同。因此，使用现有的模型很难预测机器人在低机动性条件下的轻量化行为。该项目的目标是为轻型地面机器人开发包含所有重要动力学的车辆-地形模型，并将这些模型与车辆设计和控制相结合，以最大限度地提高机动性。该项目将开发检测初期制动的估计方法和控制方法，以通过重塑地形-S剪切应力能力和调节拖曳载荷来避免制动。将开发一个模块化机器人平台，以获取数据，用于推导和验证车辆地形模型和轻型机器人模型的控制方法。该平台将允许快速修改车轮/履带板几何形状、地面压力和牵引杆载荷，以获得现场参数数据。更高保真的车辆-地形模型使控制方案能够包括基于模型的初始制动预测，这些控制方案触发控制模式以避免制动，并使机器人能够通过软地面取得进展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research project seeks fundamental understanding of the dynamics of wheeled vehicles moving through soft terrain such as snow and sand. The project will derive models of movement that incorporate improved descriptions of interactions between the wheels and the ground. These innovative models will allow the treatment of, for example, lightweight vehicles and easily crumbled terrain. These models will allow the robot to predict when it is in danger of getting irrevocably stuck. New model-based control techniques will then allow the robot to avoid this danger, for example, by repeated compaction of the terrain, appropriate modulation of cable forces from a towed load, or simply revising the planned route. The results will be deployed in mobile ground robot systems capable of traversing Arctic regions untended, carrying or towing sensor packages to provide "ground truth" data for comparison to satellite or aircraft measurements. No lightweight autonomous mobile platform currently exists that can roam Arctic terrain autonomously for long periods of time, without becoming immobilized by pockets of low cohesion snow. This project builds upon the Investigator's success with similar robots used in the more uniform and predictable terrain of the Antarctic. This project advances the national health, prosperity, and welfare by enabling long-range, long-duration scientific surveys of the Arctic region, that will provide improved understanding of the response of Arctic systems to natural and human activity. This project also supports the NSF Big Idea on Navigating the New Arctic. Finally, the project will support new content in the interdisciplinary engineering design experience for Dartmouth undergraduates, and will provide new material to a hands-on Arctic science outreach program for high school students from the US, Denmark, and Greenland.Existing vehicle-terrain models have been derived and validated for heavy vehicles, and it has been shown that the behavior of lightweight vehicles differs from what these models predict. As a result, it is difficult to anticipate lightweight robot behavior in low mobility conditions using current models. The goal of this project is to develop vehicle-terrain models for lightweight ground robots that includes all the important dynamics, and integrates those models with vehicle design and control to maximize mobility. The project will develop estimation methods to detect incipient immobilization and control methods to avoid immobilization by reshaping the terrain?s shear stress capacity and modulating towed load. A modular robotic platform will be developed to acquire data for deriving and validating vehicle-terrain models and control approaches for lightweight robot models. The platform will allow rapid modification of wheel/grouser geometry, ground pressure, and drawbar load to obtain parametric data in situ. Higher fidelity vehicle-terrain models enable control schemes comprised of model-based prediction of incipient immobilization that trigger control modes to avoid immobilization and enable a robot to make forward progress through soft terrain.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.